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Engineering protein developability

工程蛋白质可开发性

基本信息

批准号：
10681310
负责人：
Benjamin Hackel
金额：
$ 33.82万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2026-05-31
项目状态：
未结题

项目摘要

Project Summary Engineered proteins drive biotechnology and biology as therapeutics, diagnostics, and reagents. While engineering the primary function – e.g. binding – has become relatively robust, identifying proteins that meet the rigors of clinical and practical use remains highly problematic. Many proteins suffer from poor developability – instability, insolubility, low expression, and non-specific binding – that ultimately limits utility. Protein sequence space is immense, and sequence-function relationships are complex. Thus, more efficient methods are needed to map the sequence-developability landscape and reduce the practical burden of identifying developable sequences. Robust, quantitative knowledge of the landscape would [1] empower design of libraries constrained to developable space, [2] enable design of mutants to rescue lead molecules with compelling primary function but developability liabilities, and [3] enhance fundamental insight of factors that dictate protein robustness. Efficient techniques could also [4] enable integrated, upstream library-scale selection for developability. Sequence models are moderately predictive of select metrics but do not robustly quantify the overall landscape. Current experimental approaches are inefficient. Thus, creation and implementation of a platform for library-scale evaluation of protein developability would be transformative to accelerate and streamline the protein discovery and engineering pipeline. We will pursue this objective via three specific aims. Aim 1: Engineer a platform for library-scale evaluation of protein developability. We will develop a set of cellular assays that couple [i] genotype-phenotype linkage, [ii] phenotypic stratification via flow cytometric sorting or growth competition, and [iii] deep sequencing to efficiently quantify metrics of developability for millions of protein variants thereby elevating developability characterization by orders of magnitude relative to current methods. Aim 2: Elucidate sequence/developability landscapes for binder scaffolds. We will quantitatively elucidate sequence-developability landscapes for three ligand scaffolds to [i] empower mutant design to rescue lead molecules with compelling primary function but developability liabilities and [ii] to advance fundamental understanding of the physicochemical principles that dictate protein robustness. Aim 3: Design constrained libraries that yield significantly more developable binders. We will use this insight to design and test constrained combinatorial libraries to yield significantly more developable binders than an unconstrained library. We will test three hypotheses: [i] nested sampling enables the efficient traversal of the sequence/developability landscape to identify an effective constrained library design; [ii] developable space is more evolvable than naïve space (provided library scale diversity is maintained); and [iii] the intersection of developability and evolvability can be effectively identified via these methods.

项目概要工程蛋白质推动生物技术和生物学作为治疗、诊断和试剂。尽管设计主要功能——例如结合——已经变得相对强大，识别出符合临床和实际使用的严格性仍然存在很大问题。许多蛋白质的可开发性较差 – 不稳定性、不溶性、低表达和非特异性结合——最终限制了实用性。蛋白质序列空间巨大，序列函数关系复杂。因此，需要更有效的方法绘制序列-可开发性景观并减少识别可开发性的实际负担序列。对景观的可靠、定量的了解将[1]增强图书馆设计的能力为了可开发的空间，[2] 能够设计突变体来拯救具有引人注目的主要功能的先导分子但可开发性不利，并且 [3] 增强对决定蛋白质的因素的基本了解鲁棒性。高效的技术还可以 [4] 实现集成的上游文库规模选择可开发性。序列模型可以适度预测选定的指标，但不能稳健地量化整体景观。目前的实验方法效率低下。因此，创建和实施图书馆规模的蛋白质可开发性评估平台将具有变革性，以加速和简化蛋白质发现和工程流程。我们将通过三个具体目标来实现这一目标。目标 1：设计一个用于图书馆规模的蛋白质可开发性评估平台。我们将开发一套 [i] 基因型-表型连锁耦合的细胞测定，[ii] 通过流式细胞分选进行表型分层或增长竞争，以及[iii]深度测序，以有效量化数百万的可开发性指标蛋白质变体从而将可开发性表征相对于当前的数量级提高了几个数量级方法。目标 2：阐明粘合剂支架的序列/可开发性景观。我们将定量地阐明三种配体支架的序列可开发性景观，以[i]使突变体设计能够拯救具有令人信服的主要功能但可开发性不足的先导分子，并且 [ii] 推进基础研究了解决定蛋白质稳健性的物理化学原理。目标 3：设计受限产生更多可开发结合物的文库。我们将利用这种见解来设计和测试受约束的组合文库比无约束的文库产生明显更多的可开发结合物。我们将测试三个假设： [i] 嵌套采样能够有效遍历序列/可开发性景观以确定有效的受限图书馆设计； [ii] 可发展空间比朴素空间更具有进化性空间（前提是保持图书馆规模多样性）； [iii] 可开发性和可演化性的交集通过这些方法可以有效识别。